| Day | Time | Title |
|---|---|---|
| April 09 | 10:00-11:30 | Introduction |
| April 09 | 11:30-11:45 | Coffee Break |
| April 09 | 11:45-13:00 | Data Formats |
| April 09 | 13:00-14:00 | Lunch Break |
| April 09 | 14:00-15:30 | Mapping I |
| April 09 | 15:30-15:45 | Coffee Break |
| April 09 | 15:45-17:00 | Spatial Wrangling |
| April 10 | 09:00-10:30 | Mapping II |
| April 10 | 10:30-10:45 | Coffee Break |
| April 10 | 10:45-12:00 | Applied Spatial Linking |
| April 10 | 12:00-13:00 | Lunch Break |
| April 10 | 13:00-14:30 | Spatial Autocorrelation |
| April 10 | 14:30-14:45 | Coffee Break |
| April 10 | 14:45-16:00 | Spatial Econometrics & Outlook |
Spatial Wrangling
GESIS Workshop: Introduction to Geospatial Techniques for Social Scientists in R
Stefan Jünger & Dennis Abel
2025-04-09
Now
Mini wrap-up
Thus far, we have learned about
- different data formats
- how to load them
- first steps in interacting with them
- creating maps with
tmap
But the real magic in geospatial data lies in their flexibility.
Our plan for this afternoon
In this session, you will learn
- how to wrangle the different geospatial data formats even further
- how to converse from one format into the other
Because we want to
- link/join different datasets
- do some spatial analysis
1. Advanced Importing
Importing of non-spatial data
Say, the data we want to use are not available as a shapefile. Point coordinates are often stored in table formats like .csv – as is the location of charging stations for electric cars data in our ./data folder.
# A tibble: 6 × 7
operator federal_state latitude longitude power_kw type num_plugs
<chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
1 deer GmbH Baden-Württe… 48.3 9.72 44 Norm… 2
2 EnBW mobility+ AG u… Baden-Württe… 48.6 9.87 93 Schn… 2
3 SWU Energie GmbH Baden-Württe… 48.5 10.2 44 Norm… 2
4 SWU Energie GmbH Baden-Württe… 48.6 10.1 44 Norm… 2
5 SWU Energie GmbH Baden-Württe… 48.2 10.1 22 Norm… 1
6 EnBW mobility+ AG u… Baden-Württe… 48.5 9.98 30 Norm… 2From data table to geospatial data
We see that besides our attributes (e.g., operator, power,…), the table contains the two variables “longitude” (X) and “latitude” (Y), our point coordinates. When using the command sf::st_as_sf(), it is easy to transform the table into a point layer.
# transform to spatial data frame
echarging_sf <-
sf::st_as_sf(
echarging_df |>
# there were some missings in my data which is not allowed
dplyr::filter(!is.na(longitude) & !is.na(latitude)),
coords = c("longitude", "latitude")
)
# inspect data
class(echarging_sf)[1] "sf" "tbl_df" "tbl" "data.frame"Final check
Check the CRS!
Make sure to use the option crs = [EPSG_ID]. If not used, your CRS will not be defined, and you can’t perform further commands depending on the CRS. Here, I tried EPSG IO or http://projfinder.com/ to find out.
… and the other way round
Do you want to go back to handling a simple data frame? You can quickly achieve this by dropping the geometry column.
APIs
Geospatial data tend to be quite big, there’s a pressure to distribute data efficiently. Data dumps (on the internet) may not be helpful
- When resources are low
- Time’s a factor
- The data have a large geographic extent
Instead, a Programming Application Interface (API) is often used.
Data providers offering geospatial data APIs
- OpenStreetMap
- Bing
- Copernicus Climate Data Store
- …
- Cologne’s Open Data Portal
- Specialized
Rpackages, such as thewiesbadenpackage or thetidycensuspackage
Example: access to public transport
Say, we’re interested in the accessibility of public transport in Cologne
- Bus, tram, etc.
- All platforms and vehicles should be wheel-chair accessible
We can gather this information using OpenStreetMap!
Accessing OSM data: the Overpass API
The Overpass API (formerly known as OSM Server Side Scripting, or OSM3S before 2011) is a read-only API that serves up custom selected parts of the OSM map data. It acts as a database over the web: the client sends a query to the API and returns the data set that corresponds to the query.
Source: https://wiki.openstreetmap.org/wiki/Overpass_API
Starting with a geographic area to query
Many geospatial API requests start with a bounding box or another geographical extent to define which area should be accessed.
Defining some technical details
The Overpass API also requires a timeout parameter that repeats the request for a while if anything fails.
$bbox
[1] "50.8304399,6.7725303,51.0849743,7.162028"
$prefix
[1] "[out:xml][timeout:2500];\n(\n"
$suffix
[1] ");\n(._;>;);\nout body;"
$features
NULL
$osm_types
[1] "node" "way" "relation"
attr(,"class")
[1] "list" "overpass_query"
attr(,"nodes_only")
[1] FALSETurning to the content
The content we aim to request is defined with key/value pairs. It’s best to learn them by looking them up in the official documentation.
cologne_pt_stops <-
cologne_pt_stops |>
osmdata::add_osm_feature(key = "public_transport", value = "stop_position")
cologne_pt_stops$bbox
[1] "50.8304399,6.7725303,51.0849743,7.162028"
$prefix
[1] "[out:xml][timeout:2500];\n(\n"
$suffix
[1] ");\n(._;>;);\nout body;"
$features
[1] "[\"public_transport\"=\"stop_position\"]"
$osm_types
[1] "node" "way" "relation"
attr(,"class")
[1] "list" "overpass_query"
attr(,"nodes_only")
[1] FALSEConduct actual request/download
The data is finally downloaded in the osmdata package, e.g., using the osmdata::osmdata_sf() function.
Filter and transform
The data comprises a list that can be accessed as any list in R. Here, we extract the points and wrangle them (spatially).
cologne_pt_stops <-
cologne_pt_stops$osm_points |>
tibble::as_tibble() |>
sf::st_as_sf() |>
dplyr::filter(wheelchair == "yes")
cologne_pt_stopsSimple feature collection with 593 features and 95 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6.776977 ymin: 50.83381 xmax: 7.161797 ymax: 51.08398
Geodetic CRS: WGS 84
# A tibble: 593 × 96
osm_id name FIXME `VRS:gemeinde` `VRS:name` `VRS:old_ref` `VRS:ortsteil`
* <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 361716 Eifelp… <NA> KÖLN <NA> <NA> Innenstadt
2 388550 Brahms… <NA> KÖLN <NA> <NA> Lindenthal
3 21033643 Weinsb… <NA> KÖLN Weinsberg… <NA> Ehrenfeld
4 27042401 Markt <NA> BERGISCH GLAD… Bergisch … <NA> Mitte
5 28122005 Heumar… <NA> KÖLN <NA> <NA> Innenstadt
6 28301370 Kalker… <NA> KÖLN Kalker Fr… <NA> Merheim
7 28301373 Merheim <NA> KÖLN <NA> <NA> Merheim
8 28301416 Refrath <NA> BERGISCH GLAD… <NA> <NA> Refrath
9 30145575 Köln-H… <NA> KÖLN Holweide … <NA> Holweide
10 30388742 Mülhei… <NA> <NA> <NA> <NA> <NA>
# ℹ 583 more rows
# ℹ 89 more variables: `VRS:ref` <chr>, alt_name <chr>, ashtray <chr>,
# bench <chr>, bin <chr>, bus <chr>, bus_bay <chr>, bus_lines <chr>,
# check_date <chr>, `check_date:crossing` <chr>, construction <chr>,
# `construction:railway` <chr>, `construction:tram` <chr>, covered <chr>,
# crossing <chr>, departures_board <chr>, description <chr>, direction <chr>,
# disused <chr>, exit <chr>, fixme <chr>, `gtfs:name` <chr>, …The data indeed are mappable
Example: GeoJSON files
JSON files are pretty popular
- standardized and well-structured
- similar to XML-files, but human readability is a bit better
- also easy to parse for editors and browser
There’s also a JSON flavor for geospatial data…
GeoJSON snippet
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"id": 12,
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
6.957362270020273,
50.94308762750329
]
...Source: https://www.offenedaten-koeln.de/
An application from Cologne’s Open Data portal
park_and_ride <-
glue::glue(
"https://geoportal.stadt-koeln.de/arcgis/rest/services/verkehr/",
"verkehrskalender/MapServer/8/query?where=objectid+is+not+null&text=&",
"objectIds=&time=&geometry=&geometryType=esriGeometryEnvelope&inSR=&",
"spatialRel=esriSpatialRelIntersects&distance=&units=esriSRUnit_Foot&",
"relationParam=&outFields=*&returnGeometry=true&returnTrueCurves=false&",
"maxAllowableOffset=&geometryPrecision=&outSR=4326&havingClause=&",
"returnIdsOnly=false&returnCountOnly=false&orderByFields=&",
"groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&",
"gdbVersion=&historicMoment=&returnDistinctValues=false&resultOffset=&",
"resultRecordCount=&returnExtentOnly=false&datumTransformation=&",
"parameterValues=&rangeValues=&quantizationParameters=&featureEncoding=",
"esriDefault&f=pjson"
) |>
sf::st_read(as_tibble = TRUE)Reading layer `ESRIJSON' from data source
`https://geoportal.stadt-koeln.de/arcgis/rest/services/verkehr/verkehrskalender/MapServer/8/query?where=objectid+is+not+null&text=&objectIds=&time=&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&distance=&units=esriSRUnit_Foot&relationParam=&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=4326&havingClause=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&historicMoment=&returnDistinctValues=false&resultOffset=&resultRecordCount=&returnExtentOnly=false&datumTransformation=¶meterValues=&rangeValues=&quantizationParameters=&featureEncoding=esriDefault&f=pjson'
using driver `ESRIJSON'
Simple feature collection with 24 features and 9 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6.814912 ymin: 50.84826 xmax: 7.16169 ymax: 51.05211
Geodetic CRS: WGS 84Source: https://www.offenedaten-koeln.de/dataset/park-and-ride-anlagen-koeln
Park & ride parking spaces
With just two ‘simple’ commands, we can retrieve geospatial data about Cologne’s Park & Ride parking spaces in R. Not too bad, right?
Raster data access
It is not only vector data that can be processed through these mechanisms.
The idea is the same for raster data
- Accessing the information through URLs
- Just the downloaded data formats differ
Example: downloading German weather data
The German Weather Service provides excellent weather data in its Climate Data Center (CDC): https://opendata.dwd.de/climate_environment/CDC/. Let’s download some summer temperature data using a custom function.
download_dwd_data <- function(url, path) {
download.file(url, dest = paste0(path, "/", "lyr.1.asc.gz"))
R.utils::gunzip(
paste0(path, "/", "lyr.1.asc.gz"),
overwrite = TRUE,
remove = TRUE
)
raster_file <-
terra::rast(paste0(path, "/", "lyr.1.asc"))
unlink(paste0(path, "/", "lyr.1.asc.gz"))
raster_file
}Voilà
2. Linking
‘Spreadsheet join’
While a lot of our previous data were points, we only had a column for the German federal states as administrative information so far. We’re now moving “a layer down” and looking at Germany on a more fine-grained spatial level: the district. We just repeat what you already have done in the exercise 1_3_1_Basic Maps: joining data just like simple spreadsheets.
Spatial join
But what can we do, if we do not have matching identifier. For example, in the German districts data and the e-charger data, there are no matching administrative identifier.
Simple feature collection with 431 features and 29 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 280371.1 ymin: 5235856 xmax: 921292.4 ymax: 6101487
Projected CRS: ETRS89 / UTM zone 32N
# A tibble: 431 × 30
ADE GF BSG ARS AGS SDV_ARS GEN BEZ IBZ BEM NBD SN_L
<int> <int> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
1 4 4 1 01001 01001 0100100000… Flen… Krei… 40 -- ja 01
2 4 4 1 01002 01002 0100200000… Kiel Krei… 40 -- ja 01
3 4 4 1 01003 01003 0100300000… Lübe… Krei… 40 -- ja 01
4 4 4 1 01004 01004 0100400000… Neum… Krei… 40 -- ja 01
5 4 4 1 01051 01051 0105100440… Dith… Kreis 42 -- ja 01
6 4 4 1 01053 01053 0105301001… Herz… Kreis 42 -- ja 01
7 4 4 1 01054 01054 0105400560… Nord… Kreis 42 -- ja 01
8 4 4 1 01055 01055 0105500120… Osth… Kreis 42 -- ja 01
9 4 4 1 01056 01056 0105600390… Pinn… Kreis 42 -- ja 01
10 4 4 1 01057 01057 0105700570… Plön Kreis 42 -- ja 01
# ℹ 421 more rows
# ℹ 18 more variables: SN_R <chr>, SN_K <chr>, SN_V1 <chr>, SN_V2 <chr>,
# SN_G <chr>, FK_S3 <chr>, NUTS <chr>, ARS_0 <chr>, AGS_0 <chr>, WSK <date>,
# DEBKG_ID <chr>, geometry <MULTIPOLYGON [m]>, car_density <dbl>,
# ecar_share <chr>, publictransport_meandist <dbl>, population <dbl>,
# green_voteshare <dbl>, afd_voteshare <dbl>Simple feature collection with 60549 features and 5 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 5.243745 ymin: 47.2844 xmax: 15.54381 ymax: 55.50014
Geodetic CRS: WGS 84
# A tibble: 60,549 × 6
operator federal_state power_kw type num_plugs geometry
* <chr> <chr> <dbl> <chr> <dbl> <POINT [°]>
1 deer GmbH Baden-Württe… 44 Norm… 2 (9.72289 48.32789)
2 EnBW mobili… Baden-Württe… 93 Schn… 2 (9.87484 48.57853)
3 SWU Energie… Baden-Württe… 44 Norm… 2 (10.1934 48.52898)
4 SWU Energie… Baden-Württe… 44 Norm… 2 (10.08268 48.55354)
5 SWU Energie… Baden-Württe… 22 Norm… 1 (10.07698 48.17996)
6 EnBW mobili… Baden-Württe… 30 Norm… 2 (9.980588 48.48039)
7 SWU Energie… Baden-Württe… 22 Norm… 2 (9.985855 48.48316)
8 Albwerk Gmb… Baden-Württe… 22 Norm… 2 (9.760458 48.46448)
9 EnBW ODR AG Baden-Württe… 22 Norm… 1 (10.02912 48.49882)
10 Autohaus Bu… Baden-Württe… 22 Norm… 2 (9.774676 48.40366)
# ℹ 60,539 more rowsWe conduct a spatial join!
Adding district information to the point layer
Spatial join even easier
Say, we want to count the number of charging stations in each German district.
# adjust crs first
echarging_sf <-
sf::st_transform(echarging_sf, crs = sf::st_crs(german_districts))
# count all chargers in district
german_districts_enhanced$charger_in_districts <-
lengths(sf::st_intersects(german_districts_enhanced, echarging_sf))
german_districts_enhanced$charger_in_districts [1] 67 208 95 94 156 106 259 215 180 72 239 185 169 56 162
[16] 1223 216 121 452 92 100 48 101 69 36 199 1032 122 54 118
[31] 33 111 117 77 63 114 26 98 78 96 161 136 48 130 33
[46] 107 75 171 28 51 116 111 286 80 108 98 71 199 131 33
[61] 28 342 46 771 136 348 57 144 27 120 65 79 119 204 366
[76] 292 204 279 240 659 55 543 178 213 103 143 138 138 347 61
[91] 105 167 361 155 240 219 140 157 220 125 95 173 160 279 297
[106] 563 88 102 59 251 201 218 69 119 197 294 107 474 37 153
[121] 173 191 535 145 329 145 67 235 98 157 146 145 125 108 86
[136] 174 252 57 217 88 98 43 88 74 46 81 23 52 123 91
[151] 84 40 115 66 94 60 55 79 27 71 61 79 120 48 21
[166] 45 36 45 56 71 23 40 41 22 57 88 143 35 1304 768
[181] 457 241 391 353 217 433 156 185 105 84 232 55 202 293 209
[196] 132 199 123 306 67 173 128 96 233 194 83 383 92 129 109
[211] 175 144 131 229 244 114 231 434 96 246 337 72 782 1634 28
[226] 120 91 93 102 105 115 127 173 150 111 89 144 60 656 88
[241] 113 271 107 153 117 57 80 44 121 125 104 177 200 72 69
[256] 82 396 12 423 32 58 86 114 33 202 119 38 75 46 37
[271] 17 84 51 49 88 54 42 50 36 55 26 89 131 392 14
[286] 181 98 81 126 85 128 64 28 67 124 103 51 55 43 136
[301] 35 106 108 95 165 40 69 46 91 157 51 79 136 85 102
[316] 123 110 197 149 59 44 92 80 42 2534 33 38 20 81 96
[331] 88 32 70 62 114 42 608 73 136 26 45 113 73 81 40
[346] 115 115 192 73 108 80 183 159 123 114 234 406 147 101 95
[361] 110 371 129 82 40 99 93 30 57 89 91 91 26 72 82
[376] 82 64 30 107 56 53 19 56 27 63 43 32 44 28 69
[391] 54 32 43 93 42 33 50 63 66 29 41 0 0 0 0
[406] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[421] 0 0 0 0 0 0 0 0 1 0 0Charger count per district
3. Subsetting
Subsetting vector data
One might be interested in only one specific area of Germany, like Cologne. To subset a sf object, you can often use your usual data wrangling workflow. In this case, I know the AGS ID, and that is the only row I want to keep.
Using sf for subsetting
If you have no information about ids but only about the geolocation, you can use sf::st_touches() (or sf::st_within(), sf::st_intersect(), sf::st_crosses(), …) to identify, for example, all districts which share a border with Cologne.
Cropping data
We can use our raster data to subset our e-charging data. But first, we have to adjust the CRS again (use sf::st_crs(echarging_sf) to look up EPSG code):
Now we can crop the data:
E-charging stations in Cologne
‘Subsetting’ Raster Layers
As you know, we can subset vector data by simply filtering for specific attribute values. For example, to subset Cologne’s districts only by the one of Deutz, we can use the dplyr for sf data:
Cropping raster data
Cropping is then a method of cutting out a specific slice of a raster layer based on an input dataset or geospatial extent, such as a bounding box.
Masking
Masking is similar to cropping, yet values outside the extent are set to missing values (NA).
Combining Cropping and Masking
4. Extraction & Aggregation
Changes in terminology
In case, we only want to add one attribute from a vector dataset Y to another vector dataset X we can conduct a spatial join using sf::st_join() as before. There is nothing new to tell. In the raster data world, these kind of operations are called raster extractions.
Extracting information from raster data
Raster data are helpful when we aim to
- Apply calculations that are the same for all geometries in the dataset
- Extract information from raster fast and efficient
Do you remember these data operations?
Raster extraction
To extract the raster values at a specific point by location, we use the following:
immigrants_cologne
1 NA
2 NA
3 NA
4 NA
5 NA
6 NA
7 NA
8 NA
9 NA
10 NA
11 25.274725
12 25.274725
13 NA
14 NA
15 19.444444
16 NA
17 NA
18 NA
19 NA
20 NA
21 NA
22 NA
23 NA
24 10.000000
25 NA
26 NA
27 NA
28 NA
29 NA
30 5.389222
31 NA
32 NA
33 12.790698
34 8.737864
35 4.705882
36 NA
37 NA
38 NA
39 NA
40 NA
41 NA
42 NA
43 NA
44 NA
45 NA
46 NA
47 12.500000
48 NA
49 NA
50 29.729730
51 50.000000
52 NA
53 8.333333
54 8.333333
55 12.142857
56 NA
57 NA
58 NA
59 5.633803
60 17.647059
61 8.000000
62 NA
63 NA
64 10.526316
65 16.883117
66 21.739130
67 50.000000
68 NA
69 NA
70 NA
71 NA
72 NA
73 NA
74 NA
75 NA
76 NA
77 NA
78 NA
79 NA
80 NA
81 NA
82 NA
83 NA
84 NA
85 NA
86 NA
87 NA
88 9.677419
89 3.809524
90 NA
91 NA
92 NA
93 NA
94 NA
95 NA
96 NA
97 NA
98 NA
99 NA
100 NA
101 15.000000
102 7.894737
103 NA
104 18.000000
105 NA
106 NA
107 11.904762
108 16.666667
109 13.636364
110 NA
111 NA
112 NA
113 17.142857
114 6.818182
115 NA
116 NA
117 NA
118 17.142857
119 10.526316
120 NA
121 NA
122 33.043478
123 NA
124 13.888889
125 NA
126 NA
127 30.136986
128 NA
129 NA
130 26.086957
131 7.407407
132 7.500000
133 NA
134 14.285714
135 NA
136 75.000000
137 NA
138 15.789474
139 7.692308
140 10.828025
141 NA
142 NA
143 NA
144 NA
145 NA
146 NA
147 NA
148 NA
149 NA
150 NA
151 NA
152 NA
153 NA
154 NA
155 NA
156 NA
157 NA
158 NA
159 NA
160 NA
161 9.090909
162 NA
163 NA
164 NA
165 NA
166 NA
167 NA
168 NA
169 NA
170 29.729730
171 NA
172 NA
173 NA
174 NA
175 NA
176 17.857143
177 18.309859
178 18.309859
179 18.309859
180 18.309859
181 18.309859
182 18.309859
183 13.043478
184 13.043478
185 13.043478
186 13.043478
187 13.043478
188 13.043478
189 13.043478
190 13.043478
191 13.043478
192 6.629834
193 23.076923
194 18.181818
195 23.076923
196 23.076923
197 17.582418
198 7.951807
199 NA
200 16.858238
201 18.614719
202 16.814159
203 17.391304
204 21.904762
205 18.652850
206 9.375000
207 9.375000
208 NA
209 NA
210 NA
211 NA
212 NA
213 NA
214 NA
215 NA
216 NA
217 NA
218 NA
219 NA
220 NA
221 NA
222 NA
223 NA
224 NA
225 NA
226 NA
227 NA
228 NA
229 NA
230 NA
231 NA
232 NA
233 NA
234 NA
235 NA
236 10.582011
237 18.072289
238 8.679245
239 27.083333
240 11.976048
241 18.500000
242 13.281250
243 13.809524
244 7.594937
245 6.611570
246 12.195122
247 19.780220
248 NA
249 12.871287
250 15.384615
251 16.438356
252 22.857143
253 4.347826
254 NA
255 11.940299
256 NA
257 51.063830
258 20.720721
259 10.924370
260 NA
261 NA
262 NA
263 NA
264 NA
265 NA
266 NA
267 NA
268 NA
269 NA
270 NA
271 NA
272 NA
273 NA
274 NA
275 NA
276 NA
277 NA
278 NA
279 NA
280 NA
281 NA
282 NA
283 NA
284 NA
285 NA
286 NA
287 20.600858
288 15.636364
289 NA
290 NA
291 25.373134
292 11.956522
293 NA
294 NA
295 NA
296 25.773196
297 14.942529
298 14.534884
299 9.090909
300 30.400000
301 26.737968
302 15.853659
303 NA
304 20.000000
305 29.166667
306 NA
307 11.428571
308 27.192982
309 NA
310 NA
311 NA
312 19.473684
313 3.846154
314 9.090909
315 48.275862
316 14.110429
317 NA
318 14.210526
319 NA
320 26.315789
321 18.750000
322 6.000000
323 NA
324 6.382979
325 6.382979
326 8.602151
327 8.602151
328 NA
329 NA
330 33.834586
331 NA
332 26.785714
333 5.660377
334 29.411765
335 NA
336 NA
337 22.142857
338 NA
339 NA
340 26.885880
341 NA
342 30.303030
343 NA
344 NA
345 NA
346 13.043478
347 8.333333
348 8.333333
349 17.391304
350 7.878788
351 NA
352 NA
353 NA
354 5.714286
355 28.828829
356 NA
357 NA
358 NA
359 12.903226
360 12.000000
361 NA
362 NA
363 NA
364 NA
365 NA
366 NA
367 NA
368 NA
369 NA
370 NA
371 NA
372 NA
373 NA
374 26.315789
375 10.900474
376 28.571429
377 27.513228
378 27.513228
379 8.677686
380 24.686192
381 NA
382 NA
383 NA
384 NA
385 NA
386 NA
387 NA
388 NA
389 NA
390 NA
391 NA
392 NA
393 NA
394 NA
395 NA
396 NA
397 NA
398 NA
399 NA
400 NA
401 NA
402 NA
403 NA
404 NA
405 20.574163
406 2.684564
407 19.469027
408 10.077519
409 NA
410 NA
411 12.418301
412 23.529412
413 NA
414 15.000000
415 9.000000
416 8.943089
417 25.581395
418 25.581395
419 32.142857
420 NA
421 NA
422 NA
423 NA
424 NA
425 NA
426 NA
427 NA
428 NA
429 16.603774
430 NA
431 NA
432 NA
433 NA
434 NA
435 NA
436 NA
437 NA
438 NA
439 NA
440 NA
441 NA
442 NA
443 NA
444 NA
445 NA
446 NA
447 NA
448 NA
449 NA
450 NA
451 NA
452 NA
453 NA
454 NA
455 NA
456 NA
457 NA
458 NA
459 NA
460 NA
461 NA
462 NA
463 NA
464 NA
465 NA
466 NA
467 NA
468 NA
469 NA
470 NA
471 NA
472 NA
473 NA
474 NA
475 NA
476 NA
477 NA
478 NA
479 NA
480 NA
481 NA
482 NA
483 NA
484 NA
485 NA
486 NA
487 NA
488 NA
489 NA
490 NA
491 NA
492 NA
493 NA
494 NA
495 NA
496 NA
497 NA
498 NA
499 NA
500 NA
501 NA
502 NA
503 NA
504 NA
505 NA
506 NA
507 NA
508 NA
509 NA
510 NA
511 NA
512 NA
513 NA
514 NA
515 NA
516 NA
517 NA
518 NA
519 NA
520 NA
521 NA
522 NA
523 NA
524 NA
525 NA
526 NA
527 NA
528 NA
529 NA
530 NA
531 NA
532 NA
533 NA
534 8.823529
535 NA
536 NA
537 NA
538 NA
539 NA
540 NA
541 NA
542 NA
543 NA
544 NA
545 NA
546 NA
547 6.521739
548 NA
549 16.279070
550 10.169492
551 NA
552 NA
553 NA
554 NA
555 57.142857
556 NA
557 NA
558 NA
559 NA
560 NA
561 18.320611
562 NA
563 9.883721
564 NA
565 6.382979
566 NA
567 NA
568 NA
569 NA
570 NA
571 NA
572 NA
573 NA
574 NA
575 NA
576 NA
577 8.695652
578 60.000000
579 15.079365
580 NA
581 7.272727
582 6.818182
583 4.761905
584 3.333333
585 6.481481
586 6.285714
587 21.666667
588 10.416667
589 7.377049
590 10.526316
591 NA
592 NA
593 NA
594 21.276596
595 5.042017
596 10.650888
597 10.169492
598 NA
599 3.703704
600 43.767313
601 NA
602 NA
603 NA
604 NA
605 NA
606 NA
607 NA
608 8.000000
609 20.833333
610 5.555556
611 20.714286
612 12.121212
613 11.688312
614 23.809524
615 13.043478
616 23.004695
617 NA
618 NA
619 NA
620 24.324324
621 4.278075
622 15.131579
623 12.658228
624 24.742268
625 45.833333
626 NA
627 9.779180
628 11.666667
629 NA
630 NA
631 21.428571
632 21.428571
633 21.428571
634 21.428571
635 6.097561
636 26.595745
637 NA
638 12.121212
639 NA
640 NA
641 NA
642 26.114650
643 33.014354
644 54.237288
645 54.237288
646 19.444444
647 23.809524
648 50.000000
649 35.256410
650 NA
651 41.007194
652 11.267606
653 23.809524
654 NA
655 16.666667
656 NA
657 21.717172
658 20.670391
659 21.243523
660 NA
661 13.402062
662 28.125000
663 6.666667
664 6.666667
665 NA
666 20.661157
667 4.166667
668 23.076923
669 20.353982
670 16.000000
671 10.000000
672 11.607143
673 30.909091
674 NA
675 NA
676 NA
677 NA
678 NA
679 NA
680 NA
681 NA
682 NA
683 NA
684 NA
685 NA
686 NA
687 NA
688 NA
689 NA
690 NA
691 30.769231
692 20.430108
693 10.526316
694 46.590909
695 15.463918
696 NA
697 18.390805
698 5.217391
699 NA
700 5.555556
701 2.678571
702 37.404580
703 NA
704 26.395939
705 NA
706 5.555556
707 6.818182
708 4.347826
709 4.347826
710 33.043478
711 43.859649
712 33.039648
713 2.803738
714 7.500000
715 11.904762
716 NA
717 NA
718 32.467532
719 7.633588
720 13.861386
721 14.285714
722 NA
723 22.222222
724 12.643678
725 NA
726 NA
727 NA
728 4.761905
729 53.846154
730 NA
731 NA
732 NA
733 42.424242
734 11.111111
735 4.477612
736 6.250000
737 NA
738 NA
739 NA
740 42.857143
741 NA
742 NA
743 NA
744 NA
745 NA
746 NA
747 NA
748 NA
749 NA
750 NA
751 16.000000
752 NA
753 NA
754 NA
755 NA
756 NA
757 NA
758 NA
759 NA
760 NA
761 NA
762 NA
763 NA
764 NA
765 NA
766 NA
767 NA
768 NA
769 24.844720
770 NA
771 20.500000
772 NA
773 NA
774 31.034483
775 NA
776 NA
777 NA
778 NA
779 NA
780 NA
781 NA
782 NA
783 NA
784 NA
785 NA
786 NA
787 NA
788 NA
789 NA
790 18.750000
791 NA
792 NA
793 18.750000
794 18.750000
795 NA
796 NA
797 60.000000
798 NA
799 NA
800 NA
801 NA
802 NA
803 NA
804 NA
805 NA
806 NA
807 NA
808 NA
809 NA
810 NA
811 NA
812 NA
813 NA
814 NA
815 NA
816 25.925926
817 NA
818 7.563025
819 NA
820 61.111111
821 NA
822 NA
823 8.000000
824 NA
825 43.023256
826 NA
827 NA
828 20.289855
829 21.739130
830 NA
831 NA
832 NA
833 62.500000
834 NA
835 NA
836 NA
837 NA
838 NA
839 28.205128
840 NA
841 17.307692
842 12.837838
843 15.625000
844 27.741935
845 15.596330
846 42.000000
847 13.043478
848 48.648649
849 NA
850 20.833333
851 20.833333
852 7.936508
853 NA
854 NA
855 4.838710
856 4.838710
857 NA
858 NA
859 NA
860 NA
861 NA
862 NA
863 NA
864 NA
865 29.032258
866 NA
867 10.000000
868 NA
869 NA
870 27.027027
871 NA
872 NA
873 6.976744
874 NA
875 NA
876 4.687500Add results to existing dataset
This information can be added to an existing dataset (our points in this example):
echarging_sf_cologne$immigrant_rate_value <-
terra::extract(immigrant_rate, echarging_sf_cologne, ID = FALSE) |>
unlist()
echarging_sf_cologneSimple feature collection with 876 features and 6 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 343957.8 ymin: 5632513 xmax: 370664.4 ymax: 5661644
Projected CRS: ETRS89 / UTM zone 32N
# A tibble: 876 × 7
operator federal_state power_kw type num_plugs geometry
* <chr> <chr> <dbl> <chr> <dbl> <POINT [m]>
1 Cut! Energy… Nordrhein-We… 44 Norm… 2 (358799.3 5659476)
2 Kaufland Nordrhein-We… 93 Schn… 2 (352129.2 5661644)
3 Kaufland Nordrhein-We… 72 Schn… 3 (352135.4 5661628)
4 MEGA Kunden… Nordrhein-We… 44 Norm… 2 (352102.1 5661543)
5 MEGA Kunden… Nordrhein-We… 44 Norm… 2 (352102.1 5661543)
6 MEGA Kunden… Nordrhein-We… 44 Norm… 2 (352102.1 5661543)
7 MEGA Kunden… Nordrhein-We… 44 Norm… 2 (352102.1 5661543)
8 MEGA Kunden… Nordrhein-We… 44 Norm… 2 (352102.1 5661543)
9 Auto Thomas… Nordrhein-We… 39.6 Norm… 2 (352737.2 5633750)
10 EWE Go GmbH Nordrhein-We… 160 Schn… 2 (353129 5634718)
# ℹ 866 more rows
# ℹ 1 more variable: immigrant_rate_value <dbl>More elaborated: spatial buffers
Sometimes, extracting information 1:1 is not enough
- Too narrow
- Missing information about the surroundings of a point
Buffer extraction
We can use spatial buffers of different sizes to extract information on surroundings:
terra::extract(
immigrant_rate,
sf::st_buffer(echarging_sf_cologne, 500),
fun = mean,
na.rm = TRUE,
ID = FALSE,
raw = TRUE
) immigrants_cologne
1 NaN
2 30.689268
3 31.024269
4 30.109133
5 30.109133
6 30.109133
7 30.109133
8 30.109133
9 18.839996
10 NaN
11 18.251163
12 18.251163
13 12.480011
14 12.480011
15 18.424806
16 NaN
17 NaN
18 NaN
19 19.178225
20 NaN
21 NaN
22 NaN
23 NaN
24 10.514073
25 62.500000
26 62.500000
27 62.500000
28 100.000000
29 13.060481
30 13.757629
31 23.035654
32 23.035654
33 12.874063
34 15.714064
35 17.759908
36 15.964036
37 17.274671
38 22.558999
39 NaN
40 NaN
41 NaN
42 NaN
43 NaN
44 NaN
45 25.203057
46 21.455188
47 21.541857
48 20.330240
49 20.330240
50 17.108656
51 17.459820
52 43.825944
53 13.727532
54 13.727532
55 13.341282
56 16.776713
57 21.047340
58 9.702687
59 11.442805
60 13.724539
61 13.322034
62 20.663649
63 11.407075
64 10.034055
65 15.578801
66 16.972791
67 19.161427
68 26.867816
69 26.867816
70 26.867816
71 26.867816
72 26.867816
73 9.364526
74 13.405072
75 13.620435
76 27.272727
77 18.133372
78 14.677015
79 14.677015
80 NaN
81 15.000000
82 100.000000
83 100.000000
84 100.000000
85 14.779270
86 14.779270
87 9.876248
88 9.609889
89 9.961877
90 22.542760
91 23.789690
92 24.123839
93 20.684094
94 23.367645
95 14.572866
96 12.336835
97 14.275269
98 12.928160
99 12.928160
100 9.126074
101 9.354862
102 11.965110
103 12.511551
104 9.490522
105 9.071518
106 9.579474
107 10.778239
108 8.275924
109 13.897536
110 16.070802
111 11.480752
112 12.928155
113 32.536811
114 19.039160
115 24.478408
116 31.939751
117 31.939751
118 32.536811
119 30.280234
120 24.863629
121 24.863629
122 25.069421
123 22.289562
124 10.165677
125 14.443126
126 22.156499
127 22.014963
128 27.883306
129 14.279433
130 21.438430
131 11.816043
132 13.755265
133 21.246422
134 23.110947
135 22.167128
136 28.072219
137 17.239800
138 18.747400
139 17.423669
140 19.247369
141 7.436713
142 7.436713
143 13.106685
144 13.106685
145 NaN
146 15.188014
147 19.565217
148 15.188014
149 16.348265
150 15.188014
151 16.348265
152 15.188014
153 16.348265
154 16.348265
155 20.286358
156 21.543604
157 21.543604
158 14.376509
159 8.092493
160 7.589144
161 15.637289
162 13.999447
163 22.226983
164 7.170879
165 NaN
166 34.848485
167 34.848485
168 34.848485
169 34.848485
170 16.690038
171 19.077622
172 14.636317
173 14.636317
174 14.636317
175 14.636317
176 17.517823
177 18.121414
178 18.121414
179 18.121414
180 18.121414
181 18.121414
182 18.121414
183 18.057020
184 18.057020
185 18.242471
186 18.156316
187 18.242471
188 18.242471
189 18.242471
190 18.242471
191 18.242471
192 20.322962
193 24.603270
194 25.726868
195 24.603270
196 24.558092
197 26.255361
198 10.362776
199 10.022889
200 13.157760
201 23.463178
202 12.282973
203 20.313225
204 24.837026
205 18.532695
206 17.517177
207 17.517177
208 17.555169
209 17.555169
210 17.555169
211 17.555169
212 17.555169
213 17.555169
214 17.555169
215 17.555169
216 17.555169
217 17.555169
218 17.555169
219 17.555169
220 17.555169
221 17.555169
222 17.555169
223 17.555169
224 17.555169
225 17.555169
226 17.555169
227 17.555169
228 17.555169
229 17.555169
230 17.555169
231 17.555169
232 17.555169
233 17.555169
234 17.555169
235 15.317597
236 16.217751
237 13.972405
238 15.496273
239 16.670677
240 15.186751
241 15.217877
242 14.818024
243 14.719214
244 14.098589
245 21.577599
246 17.093786
247 16.779919
248 17.537970
249 15.139761
250 14.871517
251 26.271610
252 14.072582
253 14.129258
254 12.594627
255 16.989394
256 15.727774
257 19.089677
258 16.285333
259 15.900499
260 35.290712
261 35.290712
262 35.290712
263 35.290712
264 24.772475
265 24.772475
266 32.329089
267 15.789474
268 15.789474
269 18.357558
270 18.357558
271 34.010199
272 34.010199
273 34.010199
274 34.010199
275 34.010199
276 34.010199
277 34.010199
278 34.010199
279 34.010199
280 34.010199
281 12.840996
282 12.840996
283 12.840996
284 12.840996
285 12.840996
286 12.840996
287 12.239287
288 12.760854
289 14.702236
290 12.626195
291 34.215263
292 17.230151
293 14.940545
294 14.940545
295 17.363913
296 16.340990
297 16.063010
298 16.010140
299 15.288145
300 19.480073
301 16.506619
302 17.654480
303 17.456971
304 15.032626
305 20.492259
306 16.360149
307 15.624051
308 20.481348
309 23.464551
310 22.438974
311 22.438974
312 21.276037
313 17.000543
314 17.366188
315 23.981567
316 25.120230
317 84.848485
318 13.646955
319 11.321193
320 21.979540
321 15.152983
322 9.706929
323 56.708683
324 9.971849
325 9.971849
326 19.627202
327 19.627202
328 32.470658
329 31.198691
330 23.039739
331 47.236467
332 15.249318
333 20.520565
334 28.675118
335 20.509425
336 47.236467
337 23.357945
338 39.214676
339 18.936742
340 21.286377
341 8.266507
342 18.764553
343 14.757196
344 13.064173
345 13.064173
346 13.692316
347 15.401425
348 15.401425
349 10.875724
350 15.824836
351 8.451705
352 8.451705
353 11.524785
354 10.039274
355 17.552924
356 65.789474
357 65.789474
358 13.254952
359 21.114722
360 17.029447
361 30.673487
362 27.013731
363 10.861871
364 10.861871
365 10.861871
366 10.861871
367 10.861871
368 10.861871
369 10.861871
370 10.861871
371 10.861871
372 10.861871
373 10.861871
374 13.708125
375 17.812532
376 18.608512
377 15.550220
378 15.550220
379 18.913155
380 16.173418
381 31.149886
382 31.149886
383 31.149886
384 24.305103
385 24.305103
386 28.726909
387 23.200644
388 23.200644
389 24.186564
390 24.186564
391 24.186564
392 24.186564
393 24.186564
394 24.186564
395 24.186564
396 24.186564
397 24.186564
398 31.212990
399 31.212990
400 23.820456
401 23.820456
402 23.820456
403 23.820456
404 23.820456
405 24.060261
406 8.534757
407 13.704405
408 10.700428
409 14.099080
410 25.664434
411 18.785299
412 17.436260
413 20.345449
414 20.844770
415 15.386900
416 17.168522
417 14.320328
418 14.320328
419 17.203823
420 NaN
421 NaN
422 NaN
423 NaN
424 NaN
425 NaN
426 NaN
427 23.233950
428 23.233950
429 20.182639
430 23.964897
431 23.964897
432 100.000000
433 100.000000
434 100.000000
435 100.000000
436 100.000000
437 100.000000
438 100.000000
439 100.000000
440 100.000000
441 100.000000
442 100.000000
443 100.000000
444 100.000000
445 100.000000
446 100.000000
447 100.000000
448 100.000000
449 100.000000
450 100.000000
451 100.000000
452 100.000000
453 100.000000
454 100.000000
455 100.000000
456 100.000000
457 100.000000
458 100.000000
459 100.000000
460 100.000000
461 100.000000
462 100.000000
463 100.000000
464 100.000000
465 100.000000
466 100.000000
467 100.000000
468 100.000000
469 100.000000
470 100.000000
471 100.000000
472 100.000000
473 100.000000
474 100.000000
475 100.000000
476 100.000000
477 100.000000
478 100.000000
479 100.000000
480 100.000000
481 100.000000
482 100.000000
483 100.000000
484 100.000000
485 100.000000
486 100.000000
487 100.000000
488 100.000000
489 100.000000
490 100.000000
491 100.000000
492 100.000000
493 100.000000
494 NaN
495 11.113062
496 NaN
497 NaN
498 NaN
499 NaN
500 NaN
501 NaN
502 NaN
503 NaN
504 NaN
505 NaN
506 NaN
507 NaN
508 NaN
509 NaN
510 NaN
511 NaN
512 NaN
513 NaN
514 NaN
515 NaN
516 NaN
517 NaN
518 NaN
519 NaN
520 NaN
521 NaN
522 NaN
523 NaN
524 NaN
525 NaN
526 NaN
527 NaN
528 NaN
529 NaN
530 NaN
531 NaN
532 NaN
533 13.727655
534 13.354236
535 60.864662
536 44.772098
537 39.723602
538 39.723602
539 98.076923
540 98.076923
541 62.105263
542 62.105263
543 62.105263
544 62.105263
545 62.105263
546 62.105263
547 9.353183
548 13.993590
549 11.253390
550 8.894746
551 62.105263
552 13.159557
553 13.159557
554 13.159557
555 11.846289
556 26.818880
557 19.578483
558 11.859017
559 11.859017
560 9.242620
561 10.158558
562 11.958980
563 10.790331
564 10.525738
565 9.741421
566 10.649967
567 19.627231
568 19.627231
569 53.846154
570 8.120422
571 22.043419
572 22.043419
573 22.043419
574 22.043419
575 13.366921
576 12.777159
577 16.252081
578 20.549149
579 23.618988
580 19.867754
581 10.228215
582 6.255870
583 6.256628
584 7.117795
585 11.789633
586 6.089852
587 10.245778
588 9.135267
589 8.969029
590 7.427639
591 10.294174
592 7.026119
593 14.949885
594 13.872839
595 8.432926
596 7.369677
597 7.989067
598 7.181416
599 7.836296
600 13.415633
601 12.715183
602 12.715183
603 12.715183
604 12.715183
605 19.451122
606 13.480584
607 16.344883
608 18.629271
609 12.235893
610 11.406428
611 18.146996
612 13.100882
613 12.259738
614 16.268679
615 13.454494
616 15.301908
617 13.914263
618 15.415713
619 17.633649
620 13.269333
621 14.579759
622 14.239716
623 14.361029
624 20.448234
625 14.823154
626 65.000000
627 12.847498
628 10.303374
629 NaN
630 NaN
631 18.129126
632 18.129126
633 18.129126
634 18.129126
635 10.212428
636 10.661272
637 9.031977
638 8.800828
639 12.066649
640 12.787486
641 15.038232
642 14.871538
643 15.780669
644 40.995471
645 40.995471
646 21.265517
647 29.175205
648 31.191287
649 34.186923
650 28.196166
651 34.290973
652 23.547063
653 29.175205
654 22.028637
655 26.771218
656 28.546290
657 25.648111
658 22.185476
659 23.219860
660 31.865458
661 23.215181
662 23.062183
663 13.143755
664 13.143755
665 12.298931
666 12.690536
667 16.393625
668 19.010931
669 11.852314
670 18.370018
671 15.874996
672 10.040861
673 13.417263
674 14.145860
675 14.145860
676 14.145860
677 14.145860
678 14.145860
679 14.145860
680 14.145860
681 14.145860
682 14.145860
683 14.145860
684 12.454843
685 38.653298
686 38.653298
687 38.653298
688 38.653298
689 28.324210
690 34.714791
691 32.784513
692 26.079626
693 23.997821
694 33.036525
695 31.970639
696 40.297253
697 22.035236
698 12.657063
699 24.101173
700 12.884953
701 10.278415
702 30.000101
703 31.128699
704 29.330846
705 19.940401
706 12.884953
707 8.635181
708 13.289133
709 13.289133
710 22.956131
711 22.310157
712 30.173941
713 15.559987
714 17.300691
715 22.070435
716 17.885822
717 17.885822
718 10.605523
719 10.896273
720 25.140956
721 22.035657
722 22.484851
723 19.920496
724 11.275934
725 12.078559
726 12.005008
727 22.307356
728 10.849367
729 17.484287
730 13.566540
731 13.566540
732 17.121233
733 16.876633
734 15.377812
735 12.848985
736 13.699767
737 9.193501
738 16.669944
739 16.669944
740 32.142857
741 NaN
742 NaN
743 NaN
744 NaN
745 NaN
746 NaN
747 NaN
748 NaN
749 NaN
750 NaN
751 15.474401
752 36.363636
753 34.848485
754 17.612771
755 17.612771
756 17.612771
757 17.612771
758 56.565657
759 11.090226
760 11.090226
761 11.090226
762 11.090226
763 11.090226
764 11.090226
765 11.090226
766 11.090226
767 11.090226
768 11.090226
769 13.015290
770 56.565657
771 12.452092
772 31.740811
773 56.565657
774 31.920865
775 36.363636
776 36.363636
777 36.363636
778 36.363636
779 36.363636
780 36.363636
781 36.363636
782 36.363636
783 100.000000
784 100.000000
785 35.266683
786 34.247404
787 34.247404
788 34.247404
789 34.247404
790 34.260406
791 35.266683
792 35.266683
793 36.004230
794 37.731826
795 35.266683
796 34.214616
797 28.169265
798 31.833447
799 31.833447
800 32.660603
801 29.076582
802 30.932216
803 31.786471
804 30.932216
805 31.156846
806 31.156846
807 10.135115
808 10.135115
809 10.135115
810 10.135115
811 10.135115
812 10.135115
813 36.363636
814 36.363636
815 36.363636
816 21.184916
817 27.932543
818 15.584326
819 15.391452
820 14.481623
821 15.022787
822 26.179022
823 14.545675
824 9.627873
825 16.464436
826 16.197683
827 16.737836
828 14.678709
829 15.338412
830 15.622335
831 15.622335
832 15.622335
833 15.062795
834 17.432447
835 17.432447
836 9.232955
837 25.859851
838 25.859851
839 26.849386
840 16.705786
841 24.333115
842 24.680768
843 25.831182
844 22.823124
845 17.959382
846 26.821140
847 23.171492
848 30.924144
849 17.283298
850 15.362022
851 15.493390
852 10.046466
853 18.886424
854 13.333771
855 9.811799
856 9.888126
857 21.375721
858 22.540009
859 19.087613
860 19.087613
861 18.442048
862 18.442048
863 16.102033
864 14.746257
865 19.609447
866 15.998127
867 20.087950
868 20.583743
869 21.213259
870 22.658779
871 19.166508
872 15.654887
873 21.056426
874 19.530130
875 20.903429
876 15.252328terra::extract(
immigrant_rate,
sf::st_buffer(echarging_sf_cologne, 1000),
fun = mean,
na.rm = TRUE,
ID = FALSE,
raw = TRUE
) immigrants_cologne
1 13.529080
2 19.929001
3 19.607900
4 19.642150
5 19.642150
6 19.642150
7 19.642150
8 19.642150
9 18.302595
10 41.079433
11 14.025739
12 14.025739
13 10.286388
14 10.286388
15 15.970992
16 14.460422
17 14.460422
18 14.460422
19 16.177228
20 45.395123
21 45.395123
22 45.395123
23 45.395123
24 11.329216
25 32.173972
26 32.173972
27 32.173972
28 23.334825
29 15.152595
30 16.353176
31 17.166848
32 17.300654
33 12.400199
34 16.232756
35 14.641138
36 14.388859
37 15.886165
38 19.048553
39 NaN
40 NaN
41 NaN
42 NaN
43 NaN
44 NaN
45 15.960944
46 23.527919
47 24.629666
48 15.149836
49 15.149836
50 15.131865
51 15.362907
52 17.134482
53 13.662567
54 13.662567
55 15.898146
56 14.918616
57 16.938374
58 11.471347
59 11.729408
60 15.591820
61 14.140996
62 15.661559
63 10.980812
64 12.113624
65 15.113860
66 14.716975
67 15.507672
68 15.316302
69 15.316302
70 15.316302
71 15.316302
72 15.316302
73 10.119134
74 14.441562
75 15.003324
76 14.360505
77 13.135527
78 12.542610
79 12.542610
80 12.831605
81 15.193517
82 25.333156
83 25.333156
84 35.385552
85 16.410248
86 16.410248
87 12.385829
88 11.235300
89 12.267541
90 19.690377
91 19.690377
92 19.053614
93 19.690377
94 19.690377
95 16.565715
96 17.706569
97 15.269168
98 15.119310
99 15.119310
100 8.324908
101 8.856053
102 12.275663
103 13.382043
104 8.647714
105 8.402760
106 8.762963
107 13.040760
108 8.476846
109 13.449405
110 14.158421
111 13.732256
112 14.047107
113 21.967958
114 17.287955
115 18.973852
116 20.979583
117 20.979583
118 21.967958
119 23.137272
120 19.903420
121 19.903420
122 22.216930
123 20.029443
124 11.772214
125 10.861788
126 18.728154
127 19.509789
128 21.284422
129 14.398028
130 18.041181
131 12.779830
132 11.208671
133 17.597679
134 18.222336
135 21.284009
136 19.986349
137 20.048481
138 19.981407
139 20.187700
140 20.223886
141 13.656888
142 13.656888
143 14.886911
144 14.886911
145 15.896739
146 12.154482
147 13.073200
148 12.154482
149 13.812172
150 12.154482
151 13.812172
152 12.154482
153 13.812172
154 16.348265
155 21.464652
156 21.658955
157 21.658955
158 12.880951
159 8.281682
160 9.071311
161 17.325523
162 16.270742
163 18.408431
164 15.551314
165 7.110812
166 34.177627
167 34.177627
168 34.177627
169 34.177627
170 14.510839
171 16.507341
172 16.726791
173 16.726791
174 16.726791
175 16.726791
176 16.863387
177 17.447456
178 17.447456
179 17.447456
180 17.447456
181 17.447456
182 17.447456
183 18.483128
184 18.637448
185 18.637448
186 18.637448
187 18.598172
188 18.780315
189 18.780315
190 18.695913
191 18.598172
192 20.236247
193 20.588736
194 21.834017
195 20.588736
196 20.866374
197 22.286882
198 14.310459
199 13.675897
200 17.520898
201 21.838932
202 16.722790
203 18.882864
204 20.841537
205 21.386287
206 19.293722
207 19.293722
208 20.155043
209 20.155043
210 20.155043
211 20.155043
212 20.155043
213 20.155043
214 20.155043
215 20.155043
216 20.155043
217 20.155043
218 20.155043
219 20.155043
220 20.155043
221 20.155043
222 20.155043
223 20.155043
224 20.155043
225 20.155043
226 20.155043
227 20.155043
228 20.155043
229 20.155043
230 20.155043
231 20.155043
232 20.155043
233 20.155043
234 20.155043
235 16.781009
236 16.212651
237 17.233628
238 15.127043
239 13.691441
240 15.429402
241 14.248644
242 14.896604
243 14.579439
244 15.580156
245 19.953855
246 17.055723
247 16.733021
248 17.445029
249 16.281502
250 14.810606
251 21.399660
252 14.641620
253 15.855764
254 15.584692
255 15.972472
256 17.398875
257 15.733400
258 13.046227
259 16.194556
260 25.161658
261 25.161658
262 25.161658
263 25.161658
264 26.538657
265 26.538657
266 31.683341
267 17.385328
268 17.385328
269 18.695120
270 18.695120
271 32.924059
272 32.924059
273 32.924059
274 32.924059
275 32.924059
276 32.924059
277 32.924059
278 32.924059
279 32.924059
280 32.924059
281 14.312241
282 14.312241
283 14.312241
284 14.312241
285 14.312241
286 14.312241
287 14.199153
288 14.331740
289 15.520445
290 13.485136
291 29.449753
292 15.557177
293 16.496541
294 16.496541
295 17.023705
296 18.232128
297 16.643100
298 15.950695
299 17.992402
300 19.836864
301 16.689038
302 16.608271
303 18.820708
304 15.439799
305 17.396782
306 18.147558
307 18.241850
308 18.788584
309 21.868578
310 21.648835
311 21.648835
312 21.080038
313 19.481539
314 18.881549
315 22.136021
316 22.962252
317 64.976690
318 14.506221
319 11.587543
320 17.869369
321 17.246140
322 11.894196
323 31.774922
324 11.244498
325 11.244498
326 23.095500
327 23.095500
328 29.220682
329 30.978386
330 19.253464
331 34.473270
332 17.109129
333 22.288829
334 20.837800
335 19.629086
336 34.963890
337 19.482835
338 34.371841
339 18.822929
340 24.661054
341 9.579580
342 23.046852
343 12.464662
344 13.405247
345 13.405247
346 18.470715
347 13.117487
348 13.117487
349 12.162067
350 12.778851
351 12.729771
352 12.729771
353 17.017888
354 13.885893
355 15.552062
356 40.315291
357 44.249248
358 12.378056
359 26.264235
360 17.730731
361 21.695002
362 17.951643
363 17.089179
364 17.089179
365 17.089179
366 17.089179
367 17.089179
368 17.089179
369 17.089179
370 17.089179
371 17.089179
372 17.089179
373 17.089179
374 19.341350
375 18.164283
376 17.256104
377 17.562815
378 17.562815
379 17.717395
380 16.969341
381 18.312906
382 18.312906
383 18.312906
384 21.535150
385 21.535150
386 23.445404
387 21.897079
388 21.897079
389 20.454256
390 20.454256
391 20.454256
392 20.454256
393 20.454256
394 20.454256
395 20.454256
396 20.454256
397 20.454256
398 21.858624
399 21.858624
400 17.411482
401 17.411482
402 17.411482
403 17.411482
404 17.411482
405 20.535268
406 14.427189
407 15.372445
408 16.297346
409 15.680065
410 19.916058
411 18.570304
412 18.562651
413 22.209059
414 17.983579
415 18.736493
416 17.222956
417 19.348183
418 19.348183
419 19.717872
420 22.913727
421 21.743288
422 21.743288
423 22.913727
424 22.913727
425 30.537281
426 30.537281
427 21.813852
428 21.813852
429 17.801528
430 27.348777
431 27.348777
432 40.512566
433 40.512566
434 40.512566
435 40.512566
436 40.512566
437 40.512566
438 40.512566
439 40.512566
440 40.512566
441 40.512566
442 40.512566
443 40.512566
444 40.512566
445 40.512566
446 40.512566
447 40.512566
448 40.512566
449 40.512566
450 40.512566
451 40.512566
452 40.512566
453 40.512566
454 40.512566
455 40.512566
456 40.512566
457 40.512566
458 40.512566
459 40.512566
460 40.512566
461 40.512566
462 40.512566
463 40.512566
464 40.512566
465 40.512566
466 40.512566
467 40.512566
468 40.512566
469 40.512566
470 40.512566
471 40.512566
472 40.512566
473 40.512566
474 40.512566
475 40.512566
476 40.512566
477 40.512566
478 40.512566
479 40.512566
480 40.512566
481 40.512566
482 40.512566
483 40.512566
484 40.512566
485 40.512566
486 40.512566
487 40.512566
488 40.512566
489 40.512566
490 40.512566
491 40.512566
492 40.512566
493 40.512566
494 20.746522
495 16.288450
496 20.663572
497 20.663572
498 20.663572
499 20.663572
500 20.663572
501 20.663572
502 20.663572
503 20.663572
504 20.663572
505 20.663572
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508 20.663572
509 20.663572
510 20.663572
511 20.663572
512 20.663572
513 20.663572
514 20.663572
515 20.663572
516 20.663572
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518 20.663572
519 20.663572
520 20.663572
521 20.663572
522 20.663572
523 20.663572
524 20.663572
525 20.663572
526 20.663572
527 20.663572
528 20.663572
529 20.663572
530 20.663572
531 20.663572
532 20.663572
533 19.979493
534 19.166006
535 45.182032
536 20.539460
537 17.631342
538 17.631342
539 47.882099
540 47.882099
541 73.589262
542 73.589262
543 73.589262
544 73.589262
545 73.589262
546 73.589262
547 11.840302
548 12.647299
549 12.857020
550 11.647801
551 73.589262
552 10.433070
553 10.433070
554 10.433070
555 11.706439
556 11.768395
557 13.812919
558 13.181219
559 13.181219
560 10.302434
561 10.541067
562 12.768895
563 11.578704
564 12.523621
565 10.051243
566 10.421326
567 17.153326
568 17.153326
569 15.672649
570 9.266242
571 16.646831
572 16.646831
573 16.646831
574 16.646831
575 12.905475
576 12.407543
577 16.960057
578 15.980155
579 17.780769
580 15.583865
581 10.574860
582 7.205204
583 7.148086
584 8.253521
585 9.488469
586 7.547831
587 10.133495
588 9.373530
589 8.371785
590 9.089013
591 12.221164
592 7.702673
593 12.359854
594 12.605871
595 9.151539
596 10.162648
597 10.048579
598 6.830052
599 10.529484
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601 16.461415
602 16.461415
603 16.461415
604 16.461415
605 16.475316
606 14.186260
607 15.810996
608 15.233389
609 16.513781
610 16.038754
611 15.406476
612 16.039744
613 14.295303
614 14.195876
615 15.080113
616 14.757082
617 14.543009
618 11.283165
619 12.876242
620 13.012346
621 15.197850
622 13.364612
623 13.240551
624 13.779121
625 13.181617
626 25.248076
627 12.198407
628 10.112837
629 20.990581
630 20.990581
631 19.565107
632 19.565107
633 19.565107
634 19.565107
635 12.963362
636 10.614761
637 11.102207
638 10.274178
639 14.738221
640 13.982151
641 16.012976
642 13.991561
643 14.256662
644 33.902975
645 33.902975
646 26.667013
647 29.907590
648 30.293411
649 29.515364
650 31.935244
651 33.816946
652 22.792076
653 29.907590
654 24.850357
655 28.317023
656 29.778303
657 28.772787
658 23.612481
659 26.813957
660 28.542999
661 24.654967
662 24.943135
663 14.356966
664 14.356966
665 14.252179
666 10.961119
667 16.305189
668 15.025566
669 11.938093
670 17.232891
671 12.909037
672 11.427570
673 14.348731
674 12.388851
675 12.388851
676 12.388851
677 12.388851
678 12.388851
679 12.388851
680 12.388851
681 12.388851
682 12.388851
683 12.388851
684 11.972094
685 33.254181
686 33.254181
687 33.254181
688 33.254181
689 33.936259
690 33.492496
691 34.626248
692 29.369544
693 26.187613
694 32.468107
695 34.315131
696 32.106740
697 25.710142
698 13.913381
699 21.368421
700 15.193740
701 14.551417
702 29.867610
703 30.707543
704 29.293516
705 20.300954
706 15.193740
707 12.047799
708 13.548983
709 13.548983
710 25.744869
711 25.568477
712 30.504252
713 13.099185
714 21.090701
715 21.660643
716 12.099604
717 12.099604
718 12.278068
719 18.819584
720 25.166309
721 19.495035
722 19.939265
723 19.777921
724 9.506197
725 14.234373
726 11.298847
727 20.089796
728 11.190665
729 18.165002
730 17.678434
731 17.678434
732 17.388715
733 15.187220
734 15.050692
735 15.183775
736 14.281377
737 9.193501
738 13.284951
739 13.284951
740 23.810077
741 14.283475
742 14.283475
743 14.283475
744 14.283475
745 14.283475
746 14.283475
747 14.283475
748 14.283475
749 14.283475
750 14.283475
751 13.645311
752 35.620812
753 34.177627
754 20.633944
755 20.633944
756 20.633944
757 20.633944
758 36.284382
759 11.822191
760 11.822191
761 11.822191
762 11.822191
763 11.822191
764 11.822191
765 11.822191
766 11.822191
767 11.822191
768 11.822191
769 14.166298
770 37.115128
771 13.457708
772 23.590543
773 34.346713
774 32.700959
775 38.021027
776 38.021027
777 38.021027
778 38.021027
779 38.021027
780 38.021027
781 38.021027
782 38.021027
783 43.992838
784 43.992838
785 18.631403
786 18.430563
787 18.213301
788 18.430563
789 18.430563
790 19.101370
791 18.708063
792 18.631403
793 19.400540
794 19.155325
795 19.011379
796 19.011379
797 26.337160
798 20.580106
799 20.580106
800 20.453848
801 35.110913
802 35.017901
803 34.731418
804 34.731418
805 34.731418
806 34.429487
807 12.305978
808 12.305978
809 12.305978
810 12.305978
811 12.305978
812 12.305978
813 37.965491
814 37.965491
815 37.965491
816 19.970065
817 21.782446
818 17.565326
819 17.759829
820 12.979011
821 13.631427
822 26.179968
823 15.570621
824 9.735484
825 15.765710
826 14.742520
827 16.302873
828 14.325744
829 13.597943
830 15.662600
831 15.662600
832 15.662600
833 14.273696
834 16.615454
835 16.615454
836 18.139917
837 20.007072
838 20.007072
839 19.866546
840 16.338006
841 20.138700
842 23.486569
843 20.238439
844 22.517622
845 22.337084
846 21.817199
847 23.740825
848 21.872604
849 18.735760
850 16.698366
851 16.702122
852 12.071726
853 16.784577
854 14.664233
855 12.866827
856 12.830051
857 19.074949
858 22.005967
859 19.436823
860 19.436823
861 19.588928
862 19.588928
863 17.088811
864 15.163353
865 18.137910
866 21.761596
867 19.450480
868 19.683171
869 18.026928
870 18.283826
871 20.032893
872 17.340288
873 20.124846
874 21.680103
875 26.287342
876 18.997524Raster aggregation
We can use the same procedure to aggregate a raster dataset to a vector polygon dataset. That’s a very common use case. Let’s reload our Cologne shapefile.
cologne <-
sf::st_read(
"./data/cologne.shp",
quiet = TRUE
) |>
sf::st_transform(25832)
plot(cologne["NAME"])
Add the aggregated data
5. Conversion & Analysis
Raster to points
Points to raster
Raster to polygons
Analysis application: creating a quick ‘heatmap’
OpenStreetMap points data are nice for analyzing urban infrastructure. Let’s draw a quick ‘heatmap’ using obersvation densities.
Backup / For Home Use
Focal statistics / spatial filter
Focal statistics are another method of including information near a point in space. However, it’s applied to the whole dataset and is independent of arbitrary points we project onto a map.
- relates focal cells to surrounding cells
- vastly used in image processing
- but also applicable in social science research, as we will see
Analysis: edge detection
Source: https://en.wikipedia.org/wiki/Sobel_operator
Edges of immigrant rates
We can do that as well using a sobel filter
\[r_x = \begin{bmatrix}1 & 0 & -1 \\2 & 0 & -2 \\1 & 0 & -1\end{bmatrix} \times raster\_file \\r_y = \begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\-1 & -2 & -1\end{bmatrix}\times raster\_file \\r_{xy} = \sqrt{r_{x}^2 + r_{y}^2}\]
Implementation in R
From the official documentation:
Data preparation and application of filter
Comparison
